The transfer learning approach for chemical tasks may be performed with different types of neural network architectures, from the simplest feed-forward ANN (FFANN) [1]}, [2]} to more domain-specific graph convolutional neural networks (GCNN) [3]} [Fig. 1 (b)]. GCNN may be applied to a wide range of objects that can be represented in graph form and as a consequence do not need a separate feature extraction algorithm to work with molecules. Another advantage of these models is the ability to hold object’s information within first graph convolutional layers that can be especially useful in transfer learning tasks. GCNN models have been tested for predicting properties of materials and small organic molecules [4]}, [5]}, [6]}, [7]}, [8]}, [9]} and also showed promising results for transfer learning applications in materials science.
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